{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_325 = pd.read_excel(\"3-数据清洗后325样本数据.xlsx\", na_values=np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_325.drop(['样本编号', '时间', '产品性质：硫含量', '产品性质：辛烷值'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "label = data_325['RON损失']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "#data_325.drop(['RON损失'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_325_corr = data_325.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "output_corr = data_325_corr.iloc[:31]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "#output_corr.to_excel(\"相关系数得分表.xlsx\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "RON_corr = dict(data_325_corr.iloc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "RON_corr = sorted(RON_corr.items(), key=lambda x: abs(x[1]), reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_corr = pd.DataFrame(RON_corr[:31])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_corr.columns = ['特征变量', '相关系数']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "out_corr.to_excel(\"相关系数得分表.xlsx\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "names = [r[0] for r in RON_corr[:31]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['RON损失',\n",
       " 'S-ZORB.FT_9302.PV',\n",
       " 'S-ZORB.LC_5101.PV',\n",
       " 'S-ZORB.CAL.SPEED.PV',\n",
       " 'S-ZORB.CAL.CANGLIANG.PV',\n",
       " 'S-ZORB.LI_2104.DACA',\n",
       " 'S-ZORB.PDT_2104.PV',\n",
       " 'S-ZORB.TC_2801.PV',\n",
       " 'S-ZORB.FT_1504.TOTALIZERA.PV',\n",
       " 'S-ZORB.TE_5201.DACA',\n",
       " 'S-ZORB.FT_1503.TOTALIZERA.PV',\n",
       " 'S-ZORB.LT_3801.DACA',\n",
       " 'S-ZORB.LC_5001.PV',\n",
       " 'S-ZORB.SIS_TE_2802',\n",
       " 'S-ZORB.CAL_H2.PV',\n",
       " 'S-ZORB.TE_1106.DACA',\n",
       " 'S-ZORB.PT_6008.DACA',\n",
       " 'S-ZORB.AT-0001.DACA.PV',\n",
       " 'S-ZORB.AT-0002.DACA.PV',\n",
       " 'S-ZORB.PDI_1102.PV',\n",
       " 'S-ZORB.FC_1102.PV',\n",
       " 'S-ZORB.PDI_2102.PV',\n",
       " 'S-ZORB.FT_1204.TOTAL',\n",
       " 'S-ZORB.PC_1603.PV',\n",
       " 'S-ZORB.FC_5001.DACA',\n",
       " 'S-ZORB.PDT_1003.DACA',\n",
       " 'S-ZORB.PT_1501.PV',\n",
       " 'S-ZORB.FC_2801.PV',\n",
       " 'S-ZORB.PDI_2105.DACA',\n",
       " 'S-ZORB.TXE_2203A.DACA',\n",
       " 'S-ZORB.TXE_2202A.DACA']"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data_325_corr_30 = data_325_corr[names].loc[names]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RON损失</th>\n",
       "      <th>S-ZORB.FT_9302.PV</th>\n",
       "      <th>S-ZORB.LC_5101.PV</th>\n",
       "      <th>S-ZORB.CAL.SPEED.PV</th>\n",
       "      <th>S-ZORB.CAL.CANGLIANG.PV</th>\n",
       "      <th>S-ZORB.LI_2104.DACA</th>\n",
       "      <th>S-ZORB.PDT_2104.PV</th>\n",
       "      <th>S-ZORB.TC_2801.PV</th>\n",
       "      <th>S-ZORB.FT_1504.TOTALIZERA.PV</th>\n",
       "      <th>S-ZORB.TE_5201.DACA</th>\n",
       "      <th>...</th>\n",
       "      <th>S-ZORB.PDI_2102.PV</th>\n",
       "      <th>S-ZORB.FT_1204.TOTAL</th>\n",
       "      <th>S-ZORB.PC_1603.PV</th>\n",
       "      <th>S-ZORB.FC_5001.DACA</th>\n",
       "      <th>S-ZORB.PDT_1003.DACA</th>\n",
       "      <th>S-ZORB.PT_1501.PV</th>\n",
       "      <th>S-ZORB.FC_2801.PV</th>\n",
       "      <th>S-ZORB.PDI_2105.DACA</th>\n",
       "      <th>S-ZORB.TXE_2203A.DACA</th>\n",
       "      <th>S-ZORB.TXE_2202A.DACA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>RON损失</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.359407</td>\n",
       "      <td>-0.337434</td>\n",
       "      <td>-0.320362</td>\n",
       "      <td>0.319542</td>\n",
       "      <td>0.319517</td>\n",
       "      <td>0.319441</td>\n",
       "      <td>-0.318518</td>\n",
       "      <td>0.306107</td>\n",
       "      <td>0.301309</td>\n",
       "      <td>...</td>\n",
       "      <td>0.256928</td>\n",
       "      <td>-0.256314</td>\n",
       "      <td>-0.255888</td>\n",
       "      <td>0.254738</td>\n",
       "      <td>-0.249071</td>\n",
       "      <td>0.248268</td>\n",
       "      <td>-0.248213</td>\n",
       "      <td>-0.247440</td>\n",
       "      <td>0.246710</td>\n",
       "      <td>0.246663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S-ZORB.FT_9302.PV</th>\n",
       "      <td>0.359407</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.913923</td>\n",
       "      <td>-0.729437</td>\n",
       "      <td>0.768801</td>\n",
       "      <td>0.769078</td>\n",
       "      <td>0.769088</td>\n",
       "      <td>-0.522728</td>\n",
       "      <td>0.685421</td>\n",
       "      <td>0.711353</td>\n",
       "      <td>...</td>\n",
       "      <td>0.708469</td>\n",
       "      <td>-0.687692</td>\n",
       "      <td>-0.682869</td>\n",
       "      <td>0.556800</td>\n",
       "      <td>-0.326543</td>\n",
       "      <td>0.578159</td>\n",
       "      <td>-0.650908</td>\n",
       "      <td>-0.720172</td>\n",
       "      <td>0.821966</td>\n",
       "      <td>0.822655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S-ZORB.LC_5101.PV</th>\n",
       "      <td>-0.337434</td>\n",
       "      <td>-0.913923</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.756741</td>\n",
       "      <td>-0.795529</td>\n",
       "      <td>-0.795699</td>\n",
       "      <td>-0.795681</td>\n",
       "      <td>0.626053</td>\n",
       "      <td>-0.693937</td>\n",
       "      <td>-0.760714</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.697904</td>\n",
       "      <td>0.863679</td>\n",
       "      <td>0.856542</td>\n",
       "      <td>-0.576736</td>\n",
       "      <td>0.273957</td>\n",
       "      <td>-0.553170</td>\n",
       "      <td>0.813790</td>\n",
       "      <td>0.869975</td>\n",
       "      <td>-0.932450</td>\n",
       "      <td>-0.932978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S-ZORB.CAL.SPEED.PV</th>\n",
       "      <td>-0.320362</td>\n",
       "      <td>-0.729437</td>\n",
       "      <td>0.756741</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.973582</td>\n",
       "      <td>-0.973538</td>\n",
       "      <td>-0.973537</td>\n",
       "      <td>0.609182</td>\n",
       "      <td>-0.877280</td>\n",
       "      <td>-0.504653</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.671110</td>\n",
       "      <td>0.771416</td>\n",
       "      <td>0.679964</td>\n",
       "      <td>-0.386331</td>\n",
       "      <td>0.435631</td>\n",
       "      <td>-0.492145</td>\n",
       "      <td>0.700421</td>\n",
       "      <td>0.640255</td>\n",
       "      <td>-0.694409</td>\n",
       "      <td>-0.697035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S-ZORB.CAL.CANGLIANG.PV</th>\n",
       "      <td>0.319542</td>\n",
       "      <td>0.768801</td>\n",
       "      <td>-0.795529</td>\n",
       "      <td>-0.973582</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>-0.611780</td>\n",
       "      <td>0.920444</td>\n",
       "      <td>0.507561</td>\n",
       "      <td>...</td>\n",
       "      <td>0.780177</td>\n",
       "      <td>-0.763329</td>\n",
       "      <td>-0.687244</td>\n",
       "      <td>0.417218</td>\n",
       "      <td>-0.468348</td>\n",
       "      <td>0.483515</td>\n",
       "      <td>-0.725981</td>\n",
       "      <td>-0.647163</td>\n",
       "      <td>0.734082</td>\n",
       "      <td>0.736415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S-ZORB.LI_2104.DACA</th>\n",
       "      <td>0.319517</td>\n",
       "      <td>0.769078</td>\n",
       "      <td>-0.795699</td>\n",
       "      <td>-0.973538</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>-0.611637</td>\n",
       "      <td>0.920399</td>\n",
       "      <td>0.507692</td>\n",
       "      <td>...</td>\n",
       "      <td>0.780249</td>\n",
       "      <td>-0.763483</td>\n",
       "      <td>-0.687549</td>\n",
       "      <td>0.417195</td>\n",
       "      <td>-0.468475</td>\n",
       "      <td>0.483417</td>\n",
       "      <td>-0.726168</td>\n",
       "      <td>-0.647335</td>\n",
       "      <td>0.734299</td>\n",
       "      <td>0.736630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S-ZORB.PDT_2104.PV</th>\n",
       "      <td>0.319441</td>\n",
       "      <td>0.769088</td>\n",
       "      <td>-0.795681</td>\n",
       "      <td>-0.973537</td>\n",
       "      <td>0.999998</td>\n",
       "      <td>0.999999</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.611607</td>\n",
       "      <td>0.920375</td>\n",
       "      <td>0.507808</td>\n",
       "      <td>...</td>\n",
       "      <td>0.780190</td>\n",
       "      <td>-0.763449</td>\n",
       "      <td>-0.687526</td>\n",
       "      <td>0.417170</td>\n",
       "      <td>-0.468399</td>\n",
       "      <td>0.483463</td>\n",
       "      <td>-0.726105</td>\n",
       "      <td>-0.647332</td>\n",
       "      <td>0.734263</td>\n",
       "      <td>0.736596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S-ZORB.TC_2801.PV</th>\n",
       "      <td>-0.318518</td>\n",
       "      <td>-0.522728</td>\n",
       "      <td>0.626053</td>\n",
       "      <td>0.609182</td>\n",
       "      <td>-0.611780</td>\n",
       "      <td>-0.611637</td>\n",
       "      <td>-0.611607</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.631264</td>\n",
       "      <td>-0.530182</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.473418</td>\n",
       "      <td>0.670758</td>\n",
       "      <td>0.588851</td>\n",
       "      <td>-0.399961</td>\n",
       "      <td>0.388780</td>\n",
       "      <td>-0.304598</td>\n",
       "      <td>0.745304</td>\n",
       "      <td>0.520240</td>\n",
       "      <td>-0.505313</td>\n",
       "      <td>-0.507958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S-ZORB.FT_1504.TOTALIZERA.PV</th>\n",
       "      <td>0.306107</td>\n",
       "      <td>0.685421</td>\n",
       "      <td>-0.693937</td>\n",
       "      <td>-0.877280</td>\n",
       "      <td>0.920444</td>\n",
       "      <td>0.920399</td>\n",
       "      <td>0.920375</td>\n",
       "      <td>-0.631264</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.388149</td>\n",
       "      <td>...</td>\n",
       "      <td>0.830313</td>\n",
       "      <td>-0.634630</td>\n",
       "      <td>-0.540926</td>\n",
       "      <td>0.351690</td>\n",
       "      <td>-0.660515</td>\n",
       "      <td>0.399654</td>\n",
       "      <td>-0.652671</td>\n",
       "      <td>-0.482265</td>\n",
       "      <td>0.605555</td>\n",
       "      <td>0.607631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S-ZORB.TE_5201.DACA</th>\n",
       "      <td>0.301309</td>\n",
       "      <td>0.711353</td>\n",
       "      <td>-0.760714</td>\n",
       "      <td>-0.504653</td>\n",
       "      <td>0.507561</td>\n",
       "      <td>0.507692</td>\n",
       "      <td>0.507808</td>\n",
       "      <td>-0.530182</td>\n",
       "      <td>0.388149</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.387374</td>\n",
       "      <td>-0.661316</td>\n",
       "      <td>-0.662631</td>\n",
       "      <td>0.514822</td>\n",
       "      <td>-0.092440</td>\n",
       "      <td>0.517618</td>\n",
       "      <td>-0.596200</td>\n",
       "      <td>-0.733877</td>\n",
       "      <td>0.728669</td>\n",
       "      <td>0.730399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S-ZORB.FT_1503.TOTALIZERA.PV</th>\n",
       "      <td>0.301216</td>\n",
       "      <td>0.691973</td>\n",
       "      <td>-0.693184</td>\n",
       "      <td>-0.872819</td>\n",
       "      <td>0.921124</td>\n",
       "      <td>0.921076</td>\n",
       "      <td>0.921056</td>\n",
       "      <td>-0.619017</td>\n",
       "      <td>0.994639</td>\n",
       "      <td>0.389697</td>\n",
       "      <td>...</td>\n",
       "      <td>0.843559</td>\n",
       "      <td>-0.618712</td>\n",
       "      <td>-0.544189</td>\n",
       "      <td>0.390948</td>\n",
       "      <td>-0.651760</td>\n",
       "      <td>0.395281</td>\n",
       "      <td>-0.637004</td>\n",
       "      <td>-0.483723</td>\n",
       "      <td>0.616766</td>\n",
       "      <td>0.618722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S-ZORB.LT_3801.DACA</th>\n",
       "      <td>0.300213</td>\n",
       "      <td>0.651027</td>\n",
       "      <td>-0.669311</td>\n",
       "      <td>-0.793766</td>\n",
       "      <td>0.811275</td>\n",
       "      <td>0.811252</td>\n",
       "      <td>0.811265</td>\n",
       "      <td>-0.654819</td>\n",
       "      <td>0.825529</td>\n",
       "      <td>0.563902</td>\n",
       "      <td>...</td>\n",
       "      <td>0.592172</td>\n",
       "      <td>-0.636501</td>\n",
       "      <td>-0.565175</td>\n",
       "      <td>0.300981</td>\n",
       "      <td>-0.522790</td>\n",
       "      <td>0.469470</td>\n",
       "      <td>-0.633526</td>\n",
       "      <td>-0.558945</td>\n",
       "      <td>0.627605</td>\n",
       "      <td>0.631087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S-ZORB.LC_5001.PV</th>\n",
       "      <td>0.295825</td>\n",
       "      <td>0.774483</td>\n",
       "      <td>-0.829840</td>\n",
       "      <td>-0.626003</td>\n",
       "      <td>0.647939</td>\n",
       "      <td>0.648124</td>\n",
       "      <td>0.648068</td>\n",
       "      <td>-0.538298</td>\n",
       "      <td>0.565605</td>\n",
       "      <td>0.611291</td>\n",
       "      <td>...</td>\n",
       "      <td>0.596734</td>\n",
       "      <td>-0.735043</td>\n",
       "      <td>-0.702627</td>\n",
       "      <td>0.540911</td>\n",
       "      <td>-0.220233</td>\n",
       "      <td>0.436231</td>\n",
       "      <td>-0.682532</td>\n",
       "      <td>-0.711415</td>\n",
       "      <td>0.771505</td>\n",
       "      <td>0.771893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S-ZORB.SIS_TE_2802</th>\n",
       "      <td>-0.291186</td>\n",
       "      <td>-0.348906</td>\n",
       "      <td>0.396337</td>\n",
       "      <td>0.450471</td>\n",
       "      <td>-0.450057</td>\n",
       "      <td>-0.449798</td>\n",
       "      <td>-0.449785</td>\n",
       "      <td>0.933027</td>\n",
       "      <td>-0.528996</td>\n",
       "      <td>-0.370569</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.370751</td>\n",
       "      <td>0.409121</td>\n",
       "      <td>0.304380</td>\n",
       "      <td>-0.286096</td>\n",
       "      <td>0.385098</td>\n",
       "      <td>-0.206394</td>\n",
       "      <td>0.539720</td>\n",
       "      <td>0.245293</td>\n",
       "      <td>-0.234392</td>\n",
       "      <td>-0.237421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S-ZORB.CAL_H2.PV</th>\n",
       "      <td>0.290174</td>\n",
       "      <td>0.768692</td>\n",
       "      <td>-0.808535</td>\n",
       "      <td>-0.633739</td>\n",
       "      <td>0.582640</td>\n",
       "      <td>0.582655</td>\n",
       "      <td>0.582632</td>\n",
       "      <td>-0.441973</td>\n",
       "      <td>0.462502</td>\n",
       "      <td>0.676936</td>\n",
       "      <td>...</td>\n",
       "      <td>0.435587</td>\n",
       "      <td>-0.668799</td>\n",
       "      <td>-0.660984</td>\n",
       "      <td>0.521953</td>\n",
       "      <td>-0.094179</td>\n",
       "      <td>0.539285</td>\n",
       "      <td>-0.567790</td>\n",
       "      <td>-0.703247</td>\n",
       "      <td>0.727861</td>\n",
       "      <td>0.728056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S-ZORB.TE_1106.DACA</th>\n",
       "      <td>-0.289937</td>\n",
       "      <td>-0.782969</td>\n",
       "      <td>0.782950</td>\n",
       "      <td>0.609974</td>\n",
       "      <td>-0.654549</td>\n",
       "      <td>-0.654558</td>\n",
       "      <td>-0.654543</td>\n",
       "      <td>0.511542</td>\n",
       "      <td>-0.624600</td>\n",
       "      <td>-0.616554</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.668719</td>\n",
       "      <td>0.538917</td>\n",
       "      <td>0.561096</td>\n",
       "      <td>-0.676170</td>\n",
       "      <td>0.274677</td>\n",
       "      <td>-0.529408</td>\n",
       "      <td>0.519259</td>\n",
       "      <td>0.629510</td>\n",
       "      <td>-0.747936</td>\n",
       "      <td>-0.747625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S-ZORB.PT_6008.DACA</th>\n",
       "      <td>-0.279068</td>\n",
       "      <td>-0.397456</td>\n",
       "      <td>0.391431</td>\n",
       "      <td>0.540819</td>\n",
       "      <td>-0.510102</td>\n",
       "      <td>-0.509984</td>\n",
       "      <td>-0.509921</td>\n",
       "      <td>0.495729</td>\n",
       "      <td>-0.509244</td>\n",
       "      <td>-0.295219</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.350492</td>\n",
       "      <td>0.395618</td>\n",
       "      <td>0.261790</td>\n",
       "      <td>-0.047434</td>\n",
       "      <td>0.227130</td>\n",
       "      <td>-0.275404</td>\n",
       "      <td>0.394615</td>\n",
       "      <td>0.229896</td>\n",
       "      <td>-0.240153</td>\n",
       "      <td>-0.242327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S-ZORB.AT-0001.DACA.PV</th>\n",
       "      <td>0.278055</td>\n",
       "      <td>0.661022</td>\n",
       "      <td>-0.745484</td>\n",
       "      <td>-0.495795</td>\n",
       "      <td>0.503968</td>\n",
       "      <td>0.504122</td>\n",
       "      <td>0.503997</td>\n",
       "      <td>-0.405637</td>\n",
       "      <td>0.434673</td>\n",
       "      <td>0.533116</td>\n",
       "      <td>...</td>\n",
       "      <td>0.383307</td>\n",
       "      <td>-0.657420</td>\n",
       "      <td>-0.582583</td>\n",
       "      <td>0.224660</td>\n",
       "      <td>-0.134997</td>\n",
       "      <td>0.494645</td>\n",
       "      <td>-0.603460</td>\n",
       "      <td>-0.667610</td>\n",
       "      <td>0.661755</td>\n",
       "      <td>0.661211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S-ZORB.AT-0002.DACA.PV</th>\n",
       "      <td>0.277239</td>\n",
       "      <td>0.709786</td>\n",
       "      <td>-0.798263</td>\n",
       "      <td>-0.511614</td>\n",
       "      <td>0.514494</td>\n",
       "      <td>0.514655</td>\n",
       "      <td>0.514579</td>\n",
       "      <td>-0.443152</td>\n",
       "      <td>0.403115</td>\n",
       "      <td>0.636331</td>\n",
       "      <td>...</td>\n",
       "      <td>0.376335</td>\n",
       "      <td>-0.716835</td>\n",
       "      <td>-0.649178</td>\n",
       "      <td>0.285935</td>\n",
       "      <td>-0.068686</td>\n",
       "      <td>0.526635</td>\n",
       "      <td>-0.653967</td>\n",
       "      <td>-0.736048</td>\n",
       "      <td>0.722848</td>\n",
       "      <td>0.722762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S-ZORB.PDI_1102.PV</th>\n",
       "      <td>0.276002</td>\n",
       "      <td>0.762808</td>\n",
       "      <td>-0.879492</td>\n",
       "      <td>-0.785203</td>\n",
       "      <td>0.792018</td>\n",
       "      <td>0.792130</td>\n",
       "      <td>0.792117</td>\n",
       "      <td>-0.676159</td>\n",
       "      <td>0.686646</td>\n",
       "      <td>0.666530</td>\n",
       "      <td>...</td>\n",
       "      <td>0.621319</td>\n",
       "      <td>-0.931675</td>\n",
       "      <td>-0.828523</td>\n",
       "      <td>0.424444</td>\n",
       "      <td>-0.283224</td>\n",
       "      <td>0.439743</td>\n",
       "      <td>-0.880590</td>\n",
       "      <td>-0.787886</td>\n",
       "      <td>0.809669</td>\n",
       "      <td>0.811576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S-ZORB.FC_1102.PV</th>\n",
       "      <td>0.271923</td>\n",
       "      <td>0.817433</td>\n",
       "      <td>-0.891141</td>\n",
       "      <td>-0.608582</td>\n",
       "      <td>0.647966</td>\n",
       "      <td>0.648143</td>\n",
       "      <td>0.648106</td>\n",
       "      <td>-0.498365</td>\n",
       "      <td>0.526815</td>\n",
       "      <td>0.709347</td>\n",
       "      <td>...</td>\n",
       "      <td>0.620669</td>\n",
       "      <td>-0.765272</td>\n",
       "      <td>-0.731332</td>\n",
       "      <td>0.505239</td>\n",
       "      <td>-0.113574</td>\n",
       "      <td>0.513913</td>\n",
       "      <td>-0.714285</td>\n",
       "      <td>-0.764164</td>\n",
       "      <td>0.814707</td>\n",
       "      <td>0.814989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S-ZORB.PDI_2102.PV</th>\n",
       "      <td>0.256928</td>\n",
       "      <td>0.708469</td>\n",
       "      <td>-0.697904</td>\n",
       "      <td>-0.671110</td>\n",
       "      <td>0.780177</td>\n",
       "      <td>0.780249</td>\n",
       "      <td>0.780190</td>\n",
       "      <td>-0.473418</td>\n",
       "      <td>0.830313</td>\n",
       "      <td>0.387374</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.532542</td>\n",
       "      <td>-0.471774</td>\n",
       "      <td>0.469363</td>\n",
       "      <td>-0.468605</td>\n",
       "      <td>0.353498</td>\n",
       "      <td>-0.544868</td>\n",
       "      <td>-0.443812</td>\n",
       "      <td>0.616336</td>\n",
       "      <td>0.616978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S-ZORB.FT_1204.TOTAL</th>\n",
       "      <td>-0.256314</td>\n",
       "      <td>-0.687692</td>\n",
       "      <td>0.863679</td>\n",
       "      <td>0.771416</td>\n",
       "      <td>-0.763329</td>\n",
       "      <td>-0.763483</td>\n",
       "      <td>-0.763449</td>\n",
       "      <td>0.670758</td>\n",
       "      <td>-0.634630</td>\n",
       "      <td>-0.661316</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.532542</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.937511</td>\n",
       "      <td>-0.442106</td>\n",
       "      <td>0.239166</td>\n",
       "      <td>-0.394583</td>\n",
       "      <td>0.917998</td>\n",
       "      <td>0.868209</td>\n",
       "      <td>-0.850455</td>\n",
       "      <td>-0.851854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S-ZORB.PC_1603.PV</th>\n",
       "      <td>-0.255888</td>\n",
       "      <td>-0.682869</td>\n",
       "      <td>0.856542</td>\n",
       "      <td>0.679964</td>\n",
       "      <td>-0.687244</td>\n",
       "      <td>-0.687549</td>\n",
       "      <td>-0.687526</td>\n",
       "      <td>0.588851</td>\n",
       "      <td>-0.540926</td>\n",
       "      <td>-0.662631</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.471774</td>\n",
       "      <td>0.937511</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.491816</td>\n",
       "      <td>0.194116</td>\n",
       "      <td>-0.367651</td>\n",
       "      <td>0.875885</td>\n",
       "      <td>0.899630</td>\n",
       "      <td>-0.881517</td>\n",
       "      <td>-0.881801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S-ZORB.FC_5001.DACA</th>\n",
       "      <td>0.254738</td>\n",
       "      <td>0.556800</td>\n",
       "      <td>-0.576736</td>\n",
       "      <td>-0.386331</td>\n",
       "      <td>0.417218</td>\n",
       "      <td>0.417195</td>\n",
       "      <td>0.417170</td>\n",
       "      <td>-0.399961</td>\n",
       "      <td>0.351690</td>\n",
       "      <td>0.514822</td>\n",
       "      <td>...</td>\n",
       "      <td>0.469363</td>\n",
       "      <td>-0.442106</td>\n",
       "      <td>-0.491816</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.085170</td>\n",
       "      <td>0.333216</td>\n",
       "      <td>-0.383135</td>\n",
       "      <td>-0.510711</td>\n",
       "      <td>0.574964</td>\n",
       "      <td>0.574178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S-ZORB.PDT_1003.DACA</th>\n",
       "      <td>-0.249071</td>\n",
       "      <td>-0.326543</td>\n",
       "      <td>0.273957</td>\n",
       "      <td>0.435631</td>\n",
       "      <td>-0.468348</td>\n",
       "      <td>-0.468475</td>\n",
       "      <td>-0.468399</td>\n",
       "      <td>0.388780</td>\n",
       "      <td>-0.660515</td>\n",
       "      <td>-0.092440</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.468605</td>\n",
       "      <td>0.239166</td>\n",
       "      <td>0.194116</td>\n",
       "      <td>-0.085170</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.144512</td>\n",
       "      <td>0.355648</td>\n",
       "      <td>0.109349</td>\n",
       "      <td>-0.162573</td>\n",
       "      <td>-0.162914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S-ZORB.PT_1501.PV</th>\n",
       "      <td>0.248268</td>\n",
       "      <td>0.578159</td>\n",
       "      <td>-0.553170</td>\n",
       "      <td>-0.492145</td>\n",
       "      <td>0.483515</td>\n",
       "      <td>0.483417</td>\n",
       "      <td>0.483463</td>\n",
       "      <td>-0.304598</td>\n",
       "      <td>0.399654</td>\n",
       "      <td>0.517618</td>\n",
       "      <td>...</td>\n",
       "      <td>0.353498</td>\n",
       "      <td>-0.394583</td>\n",
       "      <td>-0.367651</td>\n",
       "      <td>0.333216</td>\n",
       "      <td>-0.144512</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.301122</td>\n",
       "      <td>-0.681102</td>\n",
       "      <td>0.528221</td>\n",
       "      <td>0.529055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S-ZORB.FC_2801.PV</th>\n",
       "      <td>-0.248213</td>\n",
       "      <td>-0.650908</td>\n",
       "      <td>0.813790</td>\n",
       "      <td>0.700421</td>\n",
       "      <td>-0.725981</td>\n",
       "      <td>-0.726168</td>\n",
       "      <td>-0.726105</td>\n",
       "      <td>0.745304</td>\n",
       "      <td>-0.652671</td>\n",
       "      <td>-0.596200</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.544868</td>\n",
       "      <td>0.917998</td>\n",
       "      <td>0.875885</td>\n",
       "      <td>-0.383135</td>\n",
       "      <td>0.355648</td>\n",
       "      <td>-0.301122</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.754319</td>\n",
       "      <td>-0.759107</td>\n",
       "      <td>-0.760451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S-ZORB.PDI_2105.DACA</th>\n",
       "      <td>-0.247440</td>\n",
       "      <td>-0.720172</td>\n",
       "      <td>0.869975</td>\n",
       "      <td>0.640255</td>\n",
       "      <td>-0.647163</td>\n",
       "      <td>-0.647335</td>\n",
       "      <td>-0.647332</td>\n",
       "      <td>0.520240</td>\n",
       "      <td>-0.482265</td>\n",
       "      <td>-0.733877</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.443812</td>\n",
       "      <td>0.868209</td>\n",
       "      <td>0.899630</td>\n",
       "      <td>-0.510711</td>\n",
       "      <td>0.109349</td>\n",
       "      <td>-0.681102</td>\n",
       "      <td>0.754319</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.913449</td>\n",
       "      <td>-0.913509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S-ZORB.TXE_2203A.DACA</th>\n",
       "      <td>0.246710</td>\n",
       "      <td>0.821966</td>\n",
       "      <td>-0.932450</td>\n",
       "      <td>-0.694409</td>\n",
       "      <td>0.734082</td>\n",
       "      <td>0.734299</td>\n",
       "      <td>0.734263</td>\n",
       "      <td>-0.505313</td>\n",
       "      <td>0.605555</td>\n",
       "      <td>0.728669</td>\n",
       "      <td>...</td>\n",
       "      <td>0.616336</td>\n",
       "      <td>-0.850455</td>\n",
       "      <td>-0.881517</td>\n",
       "      <td>0.574964</td>\n",
       "      <td>-0.162573</td>\n",
       "      <td>0.528221</td>\n",
       "      <td>-0.759107</td>\n",
       "      <td>-0.913449</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S-ZORB.TXE_2202A.DACA</th>\n",
       "      <td>0.246663</td>\n",
       "      <td>0.822655</td>\n",
       "      <td>-0.932978</td>\n",
       "      <td>-0.697035</td>\n",
       "      <td>0.736415</td>\n",
       "      <td>0.736630</td>\n",
       "      <td>0.736596</td>\n",
       "      <td>-0.507958</td>\n",
       "      <td>0.607631</td>\n",
       "      <td>0.730399</td>\n",
       "      <td>...</td>\n",
       "      <td>0.616978</td>\n",
       "      <td>-0.851854</td>\n",
       "      <td>-0.881801</td>\n",
       "      <td>0.574178</td>\n",
       "      <td>-0.162914</td>\n",
       "      <td>0.529055</td>\n",
       "      <td>-0.760451</td>\n",
       "      <td>-0.913509</td>\n",
       "      <td>0.999962</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>31 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 RON损失  S-ZORB.FT_9302.PV  S-ZORB.LC_5101.PV  \\\n",
       "RON损失                         1.000000           0.359407          -0.337434   \n",
       "S-ZORB.FT_9302.PV             0.359407           1.000000          -0.913923   \n",
       "S-ZORB.LC_5101.PV            -0.337434          -0.913923           1.000000   \n",
       "S-ZORB.CAL.SPEED.PV          -0.320362          -0.729437           0.756741   \n",
       "S-ZORB.CAL.CANGLIANG.PV       0.319542           0.768801          -0.795529   \n",
       "S-ZORB.LI_2104.DACA           0.319517           0.769078          -0.795699   \n",
       "S-ZORB.PDT_2104.PV            0.319441           0.769088          -0.795681   \n",
       "S-ZORB.TC_2801.PV            -0.318518          -0.522728           0.626053   \n",
       "S-ZORB.FT_1504.TOTALIZERA.PV  0.306107           0.685421          -0.693937   \n",
       "S-ZORB.TE_5201.DACA           0.301309           0.711353          -0.760714   \n",
       "S-ZORB.FT_1503.TOTALIZERA.PV  0.301216           0.691973          -0.693184   \n",
       "S-ZORB.LT_3801.DACA           0.300213           0.651027          -0.669311   \n",
       "S-ZORB.LC_5001.PV             0.295825           0.774483          -0.829840   \n",
       "S-ZORB.SIS_TE_2802           -0.291186          -0.348906           0.396337   \n",
       "S-ZORB.CAL_H2.PV              0.290174           0.768692          -0.808535   \n",
       "S-ZORB.TE_1106.DACA          -0.289937          -0.782969           0.782950   \n",
       "S-ZORB.PT_6008.DACA          -0.279068          -0.397456           0.391431   \n",
       "S-ZORB.AT-0001.DACA.PV        0.278055           0.661022          -0.745484   \n",
       "S-ZORB.AT-0002.DACA.PV        0.277239           0.709786          -0.798263   \n",
       "S-ZORB.PDI_1102.PV            0.276002           0.762808          -0.879492   \n",
       "S-ZORB.FC_1102.PV             0.271923           0.817433          -0.891141   \n",
       "S-ZORB.PDI_2102.PV            0.256928           0.708469          -0.697904   \n",
       "S-ZORB.FT_1204.TOTAL         -0.256314          -0.687692           0.863679   \n",
       "S-ZORB.PC_1603.PV            -0.255888          -0.682869           0.856542   \n",
       "S-ZORB.FC_5001.DACA           0.254738           0.556800          -0.576736   \n",
       "S-ZORB.PDT_1003.DACA         -0.249071          -0.326543           0.273957   \n",
       "S-ZORB.PT_1501.PV             0.248268           0.578159          -0.553170   \n",
       "S-ZORB.FC_2801.PV            -0.248213          -0.650908           0.813790   \n",
       "S-ZORB.PDI_2105.DACA         -0.247440          -0.720172           0.869975   \n",
       "S-ZORB.TXE_2203A.DACA         0.246710           0.821966          -0.932450   \n",
       "S-ZORB.TXE_2202A.DACA         0.246663           0.822655          -0.932978   \n",
       "\n",
       "                              S-ZORB.CAL.SPEED.PV  S-ZORB.CAL.CANGLIANG.PV  \\\n",
       "RON损失                                   -0.320362                 0.319542   \n",
       "S-ZORB.FT_9302.PV                       -0.729437                 0.768801   \n",
       "S-ZORB.LC_5101.PV                        0.756741                -0.795529   \n",
       "S-ZORB.CAL.SPEED.PV                      1.000000                -0.973582   \n",
       "S-ZORB.CAL.CANGLIANG.PV                 -0.973582                 1.000000   \n",
       "S-ZORB.LI_2104.DACA                     -0.973538                 0.999998   \n",
       "S-ZORB.PDT_2104.PV                      -0.973537                 0.999998   \n",
       "S-ZORB.TC_2801.PV                        0.609182                -0.611780   \n",
       "S-ZORB.FT_1504.TOTALIZERA.PV            -0.877280                 0.920444   \n",
       "S-ZORB.TE_5201.DACA                     -0.504653                 0.507561   \n",
       "S-ZORB.FT_1503.TOTALIZERA.PV            -0.872819                 0.921124   \n",
       "S-ZORB.LT_3801.DACA                     -0.793766                 0.811275   \n",
       "S-ZORB.LC_5001.PV                       -0.626003                 0.647939   \n",
       "S-ZORB.SIS_TE_2802                       0.450471                -0.450057   \n",
       "S-ZORB.CAL_H2.PV                        -0.633739                 0.582640   \n",
       "S-ZORB.TE_1106.DACA                      0.609974                -0.654549   \n",
       "S-ZORB.PT_6008.DACA                      0.540819                -0.510102   \n",
       "S-ZORB.AT-0001.DACA.PV                  -0.495795                 0.503968   \n",
       "S-ZORB.AT-0002.DACA.PV                  -0.511614                 0.514494   \n",
       "S-ZORB.PDI_1102.PV                      -0.785203                 0.792018   \n",
       "S-ZORB.FC_1102.PV                       -0.608582                 0.647966   \n",
       "S-ZORB.PDI_2102.PV                      -0.671110                 0.780177   \n",
       "S-ZORB.FT_1204.TOTAL                     0.771416                -0.763329   \n",
       "S-ZORB.PC_1603.PV                        0.679964                -0.687244   \n",
       "S-ZORB.FC_5001.DACA                     -0.386331                 0.417218   \n",
       "S-ZORB.PDT_1003.DACA                     0.435631                -0.468348   \n",
       "S-ZORB.PT_1501.PV                       -0.492145                 0.483515   \n",
       "S-ZORB.FC_2801.PV                        0.700421                -0.725981   \n",
       "S-ZORB.PDI_2105.DACA                     0.640255                -0.647163   \n",
       "S-ZORB.TXE_2203A.DACA                   -0.694409                 0.734082   \n",
       "S-ZORB.TXE_2202A.DACA                   -0.697035                 0.736415   \n",
       "\n",
       "                              S-ZORB.LI_2104.DACA  S-ZORB.PDT_2104.PV  \\\n",
       "RON损失                                    0.319517            0.319441   \n",
       "S-ZORB.FT_9302.PV                        0.769078            0.769088   \n",
       "S-ZORB.LC_5101.PV                       -0.795699           -0.795681   \n",
       "S-ZORB.CAL.SPEED.PV                     -0.973538           -0.973537   \n",
       "S-ZORB.CAL.CANGLIANG.PV                  0.999998            0.999998   \n",
       "S-ZORB.LI_2104.DACA                      1.000000            0.999999   \n",
       "S-ZORB.PDT_2104.PV                       0.999999            1.000000   \n",
       "S-ZORB.TC_2801.PV                       -0.611637           -0.611607   \n",
       "S-ZORB.FT_1504.TOTALIZERA.PV             0.920399            0.920375   \n",
       "S-ZORB.TE_5201.DACA                      0.507692            0.507808   \n",
       "S-ZORB.FT_1503.TOTALIZERA.PV             0.921076            0.921056   \n",
       "S-ZORB.LT_3801.DACA                      0.811252            0.811265   \n",
       "S-ZORB.LC_5001.PV                        0.648124            0.648068   \n",
       "S-ZORB.SIS_TE_2802                      -0.449798           -0.449785   \n",
       "S-ZORB.CAL_H2.PV                         0.582655            0.582632   \n",
       "S-ZORB.TE_1106.DACA                     -0.654558           -0.654543   \n",
       "S-ZORB.PT_6008.DACA                     -0.509984           -0.509921   \n",
       "S-ZORB.AT-0001.DACA.PV                   0.504122            0.503997   \n",
       "S-ZORB.AT-0002.DACA.PV                   0.514655            0.514579   \n",
       "S-ZORB.PDI_1102.PV                       0.792130            0.792117   \n",
       "S-ZORB.FC_1102.PV                        0.648143            0.648106   \n",
       "S-ZORB.PDI_2102.PV                       0.780249            0.780190   \n",
       "S-ZORB.FT_1204.TOTAL                    -0.763483           -0.763449   \n",
       "S-ZORB.PC_1603.PV                       -0.687549           -0.687526   \n",
       "S-ZORB.FC_5001.DACA                      0.417195            0.417170   \n",
       "S-ZORB.PDT_1003.DACA                    -0.468475           -0.468399   \n",
       "S-ZORB.PT_1501.PV                        0.483417            0.483463   \n",
       "S-ZORB.FC_2801.PV                       -0.726168           -0.726105   \n",
       "S-ZORB.PDI_2105.DACA                    -0.647335           -0.647332   \n",
       "S-ZORB.TXE_2203A.DACA                    0.734299            0.734263   \n",
       "S-ZORB.TXE_2202A.DACA                    0.736630            0.736596   \n",
       "\n",
       "                              S-ZORB.TC_2801.PV  S-ZORB.FT_1504.TOTALIZERA.PV  \\\n",
       "RON损失                                 -0.318518                      0.306107   \n",
       "S-ZORB.FT_9302.PV                     -0.522728                      0.685421   \n",
       "S-ZORB.LC_5101.PV                      0.626053                     -0.693937   \n",
       "S-ZORB.CAL.SPEED.PV                    0.609182                     -0.877280   \n",
       "S-ZORB.CAL.CANGLIANG.PV               -0.611780                      0.920444   \n",
       "S-ZORB.LI_2104.DACA                   -0.611637                      0.920399   \n",
       "S-ZORB.PDT_2104.PV                    -0.611607                      0.920375   \n",
       "S-ZORB.TC_2801.PV                      1.000000                     -0.631264   \n",
       "S-ZORB.FT_1504.TOTALIZERA.PV          -0.631264                      1.000000   \n",
       "S-ZORB.TE_5201.DACA                   -0.530182                      0.388149   \n",
       "S-ZORB.FT_1503.TOTALIZERA.PV          -0.619017                      0.994639   \n",
       "S-ZORB.LT_3801.DACA                   -0.654819                      0.825529   \n",
       "S-ZORB.LC_5001.PV                     -0.538298                      0.565605   \n",
       "S-ZORB.SIS_TE_2802                     0.933027                     -0.528996   \n",
       "S-ZORB.CAL_H2.PV                      -0.441973                      0.462502   \n",
       "S-ZORB.TE_1106.DACA                    0.511542                     -0.624600   \n",
       "S-ZORB.PT_6008.DACA                    0.495729                     -0.509244   \n",
       "S-ZORB.AT-0001.DACA.PV                -0.405637                      0.434673   \n",
       "S-ZORB.AT-0002.DACA.PV                -0.443152                      0.403115   \n",
       "S-ZORB.PDI_1102.PV                    -0.676159                      0.686646   \n",
       "S-ZORB.FC_1102.PV                     -0.498365                      0.526815   \n",
       "S-ZORB.PDI_2102.PV                    -0.473418                      0.830313   \n",
       "S-ZORB.FT_1204.TOTAL                   0.670758                     -0.634630   \n",
       "S-ZORB.PC_1603.PV                      0.588851                     -0.540926   \n",
       "S-ZORB.FC_5001.DACA                   -0.399961                      0.351690   \n",
       "S-ZORB.PDT_1003.DACA                   0.388780                     -0.660515   \n",
       "S-ZORB.PT_1501.PV                     -0.304598                      0.399654   \n",
       "S-ZORB.FC_2801.PV                      0.745304                     -0.652671   \n",
       "S-ZORB.PDI_2105.DACA                   0.520240                     -0.482265   \n",
       "S-ZORB.TXE_2203A.DACA                 -0.505313                      0.605555   \n",
       "S-ZORB.TXE_2202A.DACA                 -0.507958                      0.607631   \n",
       "\n",
       "                              S-ZORB.TE_5201.DACA  ...  S-ZORB.PDI_2102.PV  \\\n",
       "RON损失                                    0.301309  ...            0.256928   \n",
       "S-ZORB.FT_9302.PV                        0.711353  ...            0.708469   \n",
       "S-ZORB.LC_5101.PV                       -0.760714  ...           -0.697904   \n",
       "S-ZORB.CAL.SPEED.PV                     -0.504653  ...           -0.671110   \n",
       "S-ZORB.CAL.CANGLIANG.PV                  0.507561  ...            0.780177   \n",
       "S-ZORB.LI_2104.DACA                      0.507692  ...            0.780249   \n",
       "S-ZORB.PDT_2104.PV                       0.507808  ...            0.780190   \n",
       "S-ZORB.TC_2801.PV                       -0.530182  ...           -0.473418   \n",
       "S-ZORB.FT_1504.TOTALIZERA.PV             0.388149  ...            0.830313   \n",
       "S-ZORB.TE_5201.DACA                      1.000000  ...            0.387374   \n",
       "S-ZORB.FT_1503.TOTALIZERA.PV             0.389697  ...            0.843559   \n",
       "S-ZORB.LT_3801.DACA                      0.563902  ...            0.592172   \n",
       "S-ZORB.LC_5001.PV                        0.611291  ...            0.596734   \n",
       "S-ZORB.SIS_TE_2802                      -0.370569  ...           -0.370751   \n",
       "S-ZORB.CAL_H2.PV                         0.676936  ...            0.435587   \n",
       "S-ZORB.TE_1106.DACA                     -0.616554  ...           -0.668719   \n",
       "S-ZORB.PT_6008.DACA                     -0.295219  ...           -0.350492   \n",
       "S-ZORB.AT-0001.DACA.PV                   0.533116  ...            0.383307   \n",
       "S-ZORB.AT-0002.DACA.PV                   0.636331  ...            0.376335   \n",
       "S-ZORB.PDI_1102.PV                       0.666530  ...            0.621319   \n",
       "S-ZORB.FC_1102.PV                        0.709347  ...            0.620669   \n",
       "S-ZORB.PDI_2102.PV                       0.387374  ...            1.000000   \n",
       "S-ZORB.FT_1204.TOTAL                    -0.661316  ...           -0.532542   \n",
       "S-ZORB.PC_1603.PV                       -0.662631  ...           -0.471774   \n",
       "S-ZORB.FC_5001.DACA                      0.514822  ...            0.469363   \n",
       "S-ZORB.PDT_1003.DACA                    -0.092440  ...           -0.468605   \n",
       "S-ZORB.PT_1501.PV                        0.517618  ...            0.353498   \n",
       "S-ZORB.FC_2801.PV                       -0.596200  ...           -0.544868   \n",
       "S-ZORB.PDI_2105.DACA                    -0.733877  ...           -0.443812   \n",
       "S-ZORB.TXE_2203A.DACA                    0.728669  ...            0.616336   \n",
       "S-ZORB.TXE_2202A.DACA                    0.730399  ...            0.616978   \n",
       "\n",
       "                              S-ZORB.FT_1204.TOTAL  S-ZORB.PC_1603.PV  \\\n",
       "RON损失                                    -0.256314          -0.255888   \n",
       "S-ZORB.FT_9302.PV                        -0.687692          -0.682869   \n",
       "S-ZORB.LC_5101.PV                         0.863679           0.856542   \n",
       "S-ZORB.CAL.SPEED.PV                       0.771416           0.679964   \n",
       "S-ZORB.CAL.CANGLIANG.PV                  -0.763329          -0.687244   \n",
       "S-ZORB.LI_2104.DACA                      -0.763483          -0.687549   \n",
       "S-ZORB.PDT_2104.PV                       -0.763449          -0.687526   \n",
       "S-ZORB.TC_2801.PV                         0.670758           0.588851   \n",
       "S-ZORB.FT_1504.TOTALIZERA.PV             -0.634630          -0.540926   \n",
       "S-ZORB.TE_5201.DACA                      -0.661316          -0.662631   \n",
       "S-ZORB.FT_1503.TOTALIZERA.PV             -0.618712          -0.544189   \n",
       "S-ZORB.LT_3801.DACA                      -0.636501          -0.565175   \n",
       "S-ZORB.LC_5001.PV                        -0.735043          -0.702627   \n",
       "S-ZORB.SIS_TE_2802                        0.409121           0.304380   \n",
       "S-ZORB.CAL_H2.PV                         -0.668799          -0.660984   \n",
       "S-ZORB.TE_1106.DACA                       0.538917           0.561096   \n",
       "S-ZORB.PT_6008.DACA                       0.395618           0.261790   \n",
       "S-ZORB.AT-0001.DACA.PV                   -0.657420          -0.582583   \n",
       "S-ZORB.AT-0002.DACA.PV                   -0.716835          -0.649178   \n",
       "S-ZORB.PDI_1102.PV                       -0.931675          -0.828523   \n",
       "S-ZORB.FC_1102.PV                        -0.765272          -0.731332   \n",
       "S-ZORB.PDI_2102.PV                       -0.532542          -0.471774   \n",
       "S-ZORB.FT_1204.TOTAL                      1.000000           0.937511   \n",
       "S-ZORB.PC_1603.PV                         0.937511           1.000000   \n",
       "S-ZORB.FC_5001.DACA                      -0.442106          -0.491816   \n",
       "S-ZORB.PDT_1003.DACA                      0.239166           0.194116   \n",
       "S-ZORB.PT_1501.PV                        -0.394583          -0.367651   \n",
       "S-ZORB.FC_2801.PV                         0.917998           0.875885   \n",
       "S-ZORB.PDI_2105.DACA                      0.868209           0.899630   \n",
       "S-ZORB.TXE_2203A.DACA                    -0.850455          -0.881517   \n",
       "S-ZORB.TXE_2202A.DACA                    -0.851854          -0.881801   \n",
       "\n",
       "                              S-ZORB.FC_5001.DACA  S-ZORB.PDT_1003.DACA  \\\n",
       "RON损失                                    0.254738             -0.249071   \n",
       "S-ZORB.FT_9302.PV                        0.556800             -0.326543   \n",
       "S-ZORB.LC_5101.PV                       -0.576736              0.273957   \n",
       "S-ZORB.CAL.SPEED.PV                     -0.386331              0.435631   \n",
       "S-ZORB.CAL.CANGLIANG.PV                  0.417218             -0.468348   \n",
       "S-ZORB.LI_2104.DACA                      0.417195             -0.468475   \n",
       "S-ZORB.PDT_2104.PV                       0.417170             -0.468399   \n",
       "S-ZORB.TC_2801.PV                       -0.399961              0.388780   \n",
       "S-ZORB.FT_1504.TOTALIZERA.PV             0.351690             -0.660515   \n",
       "S-ZORB.TE_5201.DACA                      0.514822             -0.092440   \n",
       "S-ZORB.FT_1503.TOTALIZERA.PV             0.390948             -0.651760   \n",
       "S-ZORB.LT_3801.DACA                      0.300981             -0.522790   \n",
       "S-ZORB.LC_5001.PV                        0.540911             -0.220233   \n",
       "S-ZORB.SIS_TE_2802                      -0.286096              0.385098   \n",
       "S-ZORB.CAL_H2.PV                         0.521953             -0.094179   \n",
       "S-ZORB.TE_1106.DACA                     -0.676170              0.274677   \n",
       "S-ZORB.PT_6008.DACA                     -0.047434              0.227130   \n",
       "S-ZORB.AT-0001.DACA.PV                   0.224660             -0.134997   \n",
       "S-ZORB.AT-0002.DACA.PV                   0.285935             -0.068686   \n",
       "S-ZORB.PDI_1102.PV                       0.424444             -0.283224   \n",
       "S-ZORB.FC_1102.PV                        0.505239             -0.113574   \n",
       "S-ZORB.PDI_2102.PV                       0.469363             -0.468605   \n",
       "S-ZORB.FT_1204.TOTAL                    -0.442106              0.239166   \n",
       "S-ZORB.PC_1603.PV                       -0.491816              0.194116   \n",
       "S-ZORB.FC_5001.DACA                      1.000000             -0.085170   \n",
       "S-ZORB.PDT_1003.DACA                    -0.085170              1.000000   \n",
       "S-ZORB.PT_1501.PV                        0.333216             -0.144512   \n",
       "S-ZORB.FC_2801.PV                       -0.383135              0.355648   \n",
       "S-ZORB.PDI_2105.DACA                    -0.510711              0.109349   \n",
       "S-ZORB.TXE_2203A.DACA                    0.574964             -0.162573   \n",
       "S-ZORB.TXE_2202A.DACA                    0.574178             -0.162914   \n",
       "\n",
       "                              S-ZORB.PT_1501.PV  S-ZORB.FC_2801.PV  \\\n",
       "RON损失                                  0.248268          -0.248213   \n",
       "S-ZORB.FT_9302.PV                      0.578159          -0.650908   \n",
       "S-ZORB.LC_5101.PV                     -0.553170           0.813790   \n",
       "S-ZORB.CAL.SPEED.PV                   -0.492145           0.700421   \n",
       "S-ZORB.CAL.CANGLIANG.PV                0.483515          -0.725981   \n",
       "S-ZORB.LI_2104.DACA                    0.483417          -0.726168   \n",
       "S-ZORB.PDT_2104.PV                     0.483463          -0.726105   \n",
       "S-ZORB.TC_2801.PV                     -0.304598           0.745304   \n",
       "S-ZORB.FT_1504.TOTALIZERA.PV           0.399654          -0.652671   \n",
       "S-ZORB.TE_5201.DACA                    0.517618          -0.596200   \n",
       "S-ZORB.FT_1503.TOTALIZERA.PV           0.395281          -0.637004   \n",
       "S-ZORB.LT_3801.DACA                    0.469470          -0.633526   \n",
       "S-ZORB.LC_5001.PV                      0.436231          -0.682532   \n",
       "S-ZORB.SIS_TE_2802                    -0.206394           0.539720   \n",
       "S-ZORB.CAL_H2.PV                       0.539285          -0.567790   \n",
       "S-ZORB.TE_1106.DACA                   -0.529408           0.519259   \n",
       "S-ZORB.PT_6008.DACA                   -0.275404           0.394615   \n",
       "S-ZORB.AT-0001.DACA.PV                 0.494645          -0.603460   \n",
       "S-ZORB.AT-0002.DACA.PV                 0.526635          -0.653967   \n",
       "S-ZORB.PDI_1102.PV                     0.439743          -0.880590   \n",
       "S-ZORB.FC_1102.PV                      0.513913          -0.714285   \n",
       "S-ZORB.PDI_2102.PV                     0.353498          -0.544868   \n",
       "S-ZORB.FT_1204.TOTAL                  -0.394583           0.917998   \n",
       "S-ZORB.PC_1603.PV                     -0.367651           0.875885   \n",
       "S-ZORB.FC_5001.DACA                    0.333216          -0.383135   \n",
       "S-ZORB.PDT_1003.DACA                  -0.144512           0.355648   \n",
       "S-ZORB.PT_1501.PV                      1.000000          -0.301122   \n",
       "S-ZORB.FC_2801.PV                     -0.301122           1.000000   \n",
       "S-ZORB.PDI_2105.DACA                  -0.681102           0.754319   \n",
       "S-ZORB.TXE_2203A.DACA                  0.528221          -0.759107   \n",
       "S-ZORB.TXE_2202A.DACA                  0.529055          -0.760451   \n",
       "\n",
       "                              S-ZORB.PDI_2105.DACA  S-ZORB.TXE_2203A.DACA  \\\n",
       "RON损失                                    -0.247440               0.246710   \n",
       "S-ZORB.FT_9302.PV                        -0.720172               0.821966   \n",
       "S-ZORB.LC_5101.PV                         0.869975              -0.932450   \n",
       "S-ZORB.CAL.SPEED.PV                       0.640255              -0.694409   \n",
       "S-ZORB.CAL.CANGLIANG.PV                  -0.647163               0.734082   \n",
       "S-ZORB.LI_2104.DACA                      -0.647335               0.734299   \n",
       "S-ZORB.PDT_2104.PV                       -0.647332               0.734263   \n",
       "S-ZORB.TC_2801.PV                         0.520240              -0.505313   \n",
       "S-ZORB.FT_1504.TOTALIZERA.PV             -0.482265               0.605555   \n",
       "S-ZORB.TE_5201.DACA                      -0.733877               0.728669   \n",
       "S-ZORB.FT_1503.TOTALIZERA.PV             -0.483723               0.616766   \n",
       "S-ZORB.LT_3801.DACA                      -0.558945               0.627605   \n",
       "S-ZORB.LC_5001.PV                        -0.711415               0.771505   \n",
       "S-ZORB.SIS_TE_2802                        0.245293              -0.234392   \n",
       "S-ZORB.CAL_H2.PV                         -0.703247               0.727861   \n",
       "S-ZORB.TE_1106.DACA                       0.629510              -0.747936   \n",
       "S-ZORB.PT_6008.DACA                       0.229896              -0.240153   \n",
       "S-ZORB.AT-0001.DACA.PV                   -0.667610               0.661755   \n",
       "S-ZORB.AT-0002.DACA.PV                   -0.736048               0.722848   \n",
       "S-ZORB.PDI_1102.PV                       -0.787886               0.809669   \n",
       "S-ZORB.FC_1102.PV                        -0.764164               0.814707   \n",
       "S-ZORB.PDI_2102.PV                       -0.443812               0.616336   \n",
       "S-ZORB.FT_1204.TOTAL                      0.868209              -0.850455   \n",
       "S-ZORB.PC_1603.PV                         0.899630              -0.881517   \n",
       "S-ZORB.FC_5001.DACA                      -0.510711               0.574964   \n",
       "S-ZORB.PDT_1003.DACA                      0.109349              -0.162573   \n",
       "S-ZORB.PT_1501.PV                        -0.681102               0.528221   \n",
       "S-ZORB.FC_2801.PV                         0.754319              -0.759107   \n",
       "S-ZORB.PDI_2105.DACA                      1.000000              -0.913449   \n",
       "S-ZORB.TXE_2203A.DACA                    -0.913449               1.000000   \n",
       "S-ZORB.TXE_2202A.DACA                    -0.913509               0.999962   \n",
       "\n",
       "                              S-ZORB.TXE_2202A.DACA  \n",
       "RON损失                                      0.246663  \n",
       "S-ZORB.FT_9302.PV                          0.822655  \n",
       "S-ZORB.LC_5101.PV                         -0.932978  \n",
       "S-ZORB.CAL.SPEED.PV                       -0.697035  \n",
       "S-ZORB.CAL.CANGLIANG.PV                    0.736415  \n",
       "S-ZORB.LI_2104.DACA                        0.736630  \n",
       "S-ZORB.PDT_2104.PV                         0.736596  \n",
       "S-ZORB.TC_2801.PV                         -0.507958  \n",
       "S-ZORB.FT_1504.TOTALIZERA.PV               0.607631  \n",
       "S-ZORB.TE_5201.DACA                        0.730399  \n",
       "S-ZORB.FT_1503.TOTALIZERA.PV               0.618722  \n",
       "S-ZORB.LT_3801.DACA                        0.631087  \n",
       "S-ZORB.LC_5001.PV                          0.771893  \n",
       "S-ZORB.SIS_TE_2802                        -0.237421  \n",
       "S-ZORB.CAL_H2.PV                           0.728056  \n",
       "S-ZORB.TE_1106.DACA                       -0.747625  \n",
       "S-ZORB.PT_6008.DACA                       -0.242327  \n",
       "S-ZORB.AT-0001.DACA.PV                     0.661211  \n",
       "S-ZORB.AT-0002.DACA.PV                     0.722762  \n",
       "S-ZORB.PDI_1102.PV                         0.811576  \n",
       "S-ZORB.FC_1102.PV                          0.814989  \n",
       "S-ZORB.PDI_2102.PV                         0.616978  \n",
       "S-ZORB.FT_1204.TOTAL                      -0.851854  \n",
       "S-ZORB.PC_1603.PV                         -0.881801  \n",
       "S-ZORB.FC_5001.DACA                        0.574178  \n",
       "S-ZORB.PDT_1003.DACA                      -0.162914  \n",
       "S-ZORB.PT_1501.PV                          0.529055  \n",
       "S-ZORB.FC_2801.PV                         -0.760451  \n",
       "S-ZORB.PDI_2105.DACA                      -0.913509  \n",
       "S-ZORB.TXE_2203A.DACA                      0.999962  \n",
       "S-ZORB.TXE_2202A.DACA                      1.000000  \n",
       "\n",
       "[31 rows x 31 columns]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_325_corr_30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "colors = \"Accent, Accent_r, Blues, Blues_r, BrBG, BrBG_r, BuGn, BuGn_r, BuPu, BuPu_r, CMRmap, CMRmap_r, Dark2, Dark2_r, GnBu, GnBu_r, Greens, Greens_r, Greys, Greys_r, OrRd, OrRd_r, Oranges, Oranges_r, PRGn, PRGn_r, Paired, Paired_r, Pastel1, Pastel1_r, Pastel2, Pastel2_r, PiYG, PiYG_r, PuBu, PuBuGn, PuBuGn_r, PuBu_r, PuOr, PuOr_r, PuRd, PuRd_r, Purples, Purples_r, RdBu, RdBu_r, RdGy, RdGy_r, RdPu, RdPu_r, RdYlBu, RdYlBu_r, RdYlGn, RdYlGn_r, Reds, Reds_r, Set1, Set1_r, Set2, Set2_r, Set3, Set3_r, Spectral, Spectral_r, Wistia, Wistia_r, YlGn, YlGnBu, YlGnBu_r, YlGn_r, YlOrBr, YlOrBr_r, YlOrRd, YlOrRd_r, afmhot, afmhot_r, autumn, autumn_r, binary, binary_r, bone, bone_r, brg, brg_r, bwr, bwr_r, cividis, cividis_r, cool, cool_r, coolwarm, coolwarm_r, copper, copper_r, cubehelix, cubehelix_r, flag, flag_r, gist_earth, gist_earth_r, gist_gray, gist_gray_r, gist_heat, gist_heat_r, gist_ncar, gist_ncar_r, gist_rainbow, gist_rainbow_r, gist_stern, gist_stern_r, gist_yarg, gist_yarg_r, gnuplot, gnuplot2, gnuplot2_r, gnuplot_r, gray, gray_r, hot, hot_r, hsv, hsv_r, icefire, icefire_r, inferno, inferno_r, jet, jet_r, magma, magma_r, mako, mako_r, nipy_spectral, nipy_spectral_r, ocean, ocean_r, pink, pink_r, plasma, plasma_r, prism, prism_r, rainbow, rainbow_r, rocket, rocket_r, seismic, seismic_r, spring, spring_r, summer, summer_r, tab10, tab10_r, tab20, tab20_r, tab20b, tab20b_r, tab20c, tab20c_r, terrain, terrain_r, twilight, twilight_r, twilight_shifted, twilight_shifted_r, viridis, viridis_r, vlag, vlag_r, winter, winter_r\"\n",
    "colors_list = colors.split(\", \")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.figure(figsize=(15, 10))\n",
    "plt.title(\"特征变量相关系数热力图\")\n",
    "ax = sns.heatmap(data_325_corr_30, linewidths=0.5, cmap='OrRd', cbar=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['RON损失', '原料性质：硫含量', '原料性质：辛烷值', '原料性质：饱和烃', '原料性质：烯烃', '原料性质：芳烃',\n",
       "       '原料性质：溴值', '原料性质：密度', '产品性质：辛烷值', '待生吸附剂性质：焦炭',\n",
       "       ...\n",
       "       'S-ZORB.AT-0011.DACA.PV', 'S-ZORB.FT_1204.DACA.PV',\n",
       "       'S-ZORB.LC_5102.PIDA.PV', 'S-ZORB.TE_1102.DACA.PV',\n",
       "       'S-ZORB.CAL.LINE.PV', 'S-ZORB.CAL.CANGLIANG.PV', 'S-ZORB.CAL.SPEED.PV',\n",
       "       'S-ZORB.FT_1503.TOTALIZERA.PV', 'S-ZORB.FT_1504.TOTALIZERA.PV',\n",
       "       'S-ZORB.PC_1001A.PV'],\n",
       "      dtype='object', length=200)"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_325_corr.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_list = dict(data_325_corr.iloc[8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.2647790278283664"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_list.pop('RON损失')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr_list = sorted(corr_list.items(), key=lambda item:abs(item[1]), reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('产品性质：辛烷值', 1.0),\n",
       " ('原料性质：辛烷值', 0.9733518469806896),\n",
       " ('原料性质：硫含量', 0.49424823485378205),\n",
       " ('原料性质：饱和烃', -0.4661285827983886),\n",
       " ('S-ZORB.PT_7107B.DACA', -0.45783265541271706),\n",
       " ('S-ZORB.TE_5002.DACA', 0.4571463393417394),\n",
       " ('S-ZORB.TE_1201.PV', 0.45701971087501025),\n",
       " ('S-ZORB.PT_7103B.DACA', -0.4516957550077574),\n",
       " ('S-ZORB.TE_1101.DACA', 0.4468969085193173),\n",
       " ('S-ZORB.TE_1105.PV', 0.4245794849369569),\n",
       " ('S-ZORB.TE_7108B.DACA', -0.4068035849558528),\n",
       " ('S-ZORB.TE_7106B.DACA', -0.40053461112105304),\n",
       " ('原料性质：烯烃', 0.3963823778616483),\n",
       " ('S-ZORB.TC_2801.PV', 0.37033587424467557),\n",
       " ('S-ZORB.TE_5008.DACA', -0.3635937717382536),\n",
       " ('S-ZORB.TE_5201.DACA', -0.3398854301993403),\n",
       " ('S-ZORB.FT_3303.DACA', 0.33930818962646186),\n",
       " ('S-ZORB.SIS_TE_2802', 0.3355637026213727),\n",
       " ('S-ZORB.FC_5001.DACA', -0.32970647622985655),\n",
       " ('S-ZORB.TE_2601.PV', 0.3141159456294306),\n",
       " ('S-ZORB.TE_1106.DACA', 0.30546844508453475),\n",
       " ('S-ZORB.TE_2603.DACA', 0.29795165843318516),\n",
       " ('S-ZORB.PDT_3503.DACA', -0.2976073693115289),\n",
       " ('S-ZORB.LT_9101.DACA', -0.28976338719260325),\n",
       " ('S-ZORB.AT-0006.DACA.PV', 0.278454898763737),\n",
       " ('原料性质：密度', 0.26164106078448157),\n",
       " ('S-ZORB.SIS_FT_3202.PV', 0.2594897745021661),\n",
       " ('S-ZORB.LC_5101.PV', 0.2556018645598795),\n",
       " ('S-ZORB.LT_3801.DACA', -0.2545565305127057),\n",
       " ('S-ZORB.TXE_2202A.DACA', -0.25261415391508446),\n",
       " ('S-ZORB.PC_3501.DACA', -0.2523147899445827),\n",
       " ('S-ZORB.TXE_2203A.DACA', -0.25168304452397966),\n",
       " ('S-ZORB.AT-0008.DACA.PV', 0.24817330910269292),\n",
       " ('S-ZORB.AT-0011.DACA.PV', 0.24703123771513796),\n",
       " ('S-ZORB.PT_2106.DACA', 0.24600322165805222),\n",
       " ('S-ZORB.AT-0007.DACA.PV', 0.2437961294634657),\n",
       " ('S-ZORB.FT_9101.TOTAL', 0.24319782772464288),\n",
       " ('S-ZORB.PC_2105.PV', 0.24225676443436447),\n",
       " ('S-ZORB.FT_9202.PV', -0.24158781626624493),\n",
       " ('S-ZORB.PC_1603.PV', 0.24106985429075506),\n",
       " ('S-ZORB.FT_9402.TOTAL', 0.24021661323717275),\n",
       " ('S-ZORB.AT-0004.DACA.PV', 0.2366283078027814),\n",
       " ('S-ZORB.FT_9301.TOTAL', 0.23606697638632468),\n",
       " ('S-ZORB.FT_3301.TOTAL', 0.23526273325029512),\n",
       " ('S-ZORB.AT-0009.DACA.PV', 0.2347072882928698),\n",
       " ('S-ZORB.PDT_3601.DACA', -0.23460696579997256),\n",
       " ('S-ZORB.PDI_2105.DACA', 0.23448916310270806),\n",
       " ('S-ZORB.LC_5001.PV', -0.23428084428384235),\n",
       " ('S-ZORB.FT_9403.PV', 0.23317187306536738),\n",
       " ('S-ZORB.CAL_H2.PV', -0.2309661664224195),\n",
       " ('S-ZORB.PDI_2903.DACA', 0.22847841967096946),\n",
       " ('S-ZORB.FT_9201.TOTAL', 0.228160152211101),\n",
       " ('S-ZORB.BS_LT_2401.PV', 0.22550267650465988),\n",
       " ('S-ZORB.FT_9403.TOTAL', 0.22441165586168033),\n",
       " ('S-ZORB.PDT_1002.DACA', -0.22356229126771882),\n",
       " ('S-ZORB.TC_1607.DACA', -0.22354191743458307),\n",
       " ('S-ZORB.FT_1204.TOTAL', 0.22240346034371258),\n",
       " ('S-ZORB.FT_9302.PV', -0.2221847584098702),\n",
       " ('S-ZORB.PC_3101.DACA', 0.219758667093097),\n",
       " ('S-ZORB.PDT_1003.DACA', 0.2128737736734309),\n",
       " ('S-ZORB.PC_1001A.PV', -0.21229988396795502),\n",
       " ('S-ZORB.FT_9402.PV', 0.2072230096331382),\n",
       " ('S-ZORB.TC_5005.PV', 0.20214834628554088),\n",
       " ('S-ZORB.FT_9401.TOTAL', 0.2014192700398184),\n",
       " ('S-ZORB.FT_5201.TOTAL', 0.19964319586830132),\n",
       " ('S-ZORB.FT_1001.TOTAL', 0.19926999250785574),\n",
       " ('S-ZORB.FT_1503.TOTALIZERA.PV', -0.19881317258597467),\n",
       " ('S-ZORB.FT_9202.TOTAL', 0.19586771127456984),\n",
       " ('S-ZORB.TE_5004.DACA', 0.1903238732504616),\n",
       " ('S-ZORB.FT_1504.TOTALIZERA.PV', -0.1902308126400642),\n",
       " ('S-ZORB.PT_6006.DACA', -0.1901263724470951),\n",
       " ('S-ZORB.TC_2702.DACA', 0.1881272507518787),\n",
       " ('S-ZORB.FC_5103.DACA', -0.18686818842315925),\n",
       " ('S-ZORB.FT_5101.TOTAL', 0.18639584358166034),\n",
       " ('S-ZORB.FT_9302.TOTAL', 0.1862430866398929),\n",
       " ('S-ZORB.TXE_3202A.DACA', -0.18544211070136873),\n",
       " ('S-ZORB.PT_1103.DACA', 0.18392031167417072),\n",
       " ('S-ZORB.PDT_2001.DACA', 0.18343644842954768),\n",
       " ('S-ZORB.TXE_3201A.DACA', -0.18294262720224844),\n",
       " ('S-ZORB.TE_2005.PV', 0.18193556436890454),\n",
       " ('S-ZORB.AT-0005.DACA.PV', -0.18144951236936271),\n",
       " ('S-ZORB.FC_1101.TOTAL', 0.18118548722589609),\n",
       " ('S-ZORB.PT_9402.PV', 0.17972859748457667),\n",
       " ('S-ZORB.TE_2004.DACA', 0.1776114999012551),\n",
       " ('S-ZORB.FT_9001.TOTAL', 0.17751092197915844),\n",
       " ('S-ZORB.LT_2101.DACA', -0.17514377282756655),\n",
       " ('S-ZORB.PC_9002.DACA', -0.17503810217054339),\n",
       " ('S-ZORB.TE_5202.PV', -0.17448175868476434),\n",
       " ('S-ZORB.PT_1101.DACA', 0.1731965298036684),\n",
       " ('S-ZORB.PC_1301.PV', 0.17088068607500256),\n",
       " ('S-ZORB.FC_2702.DACA', -0.16898077357903749),\n",
       " ('S-ZORB.PT_1602A.PV', -0.16783605966415222),\n",
       " ('S-ZORB.CAL.SPEED.PV', 0.16551447751455636),\n",
       " ('S-ZORB.ZT_2634.DACA', 0.16476575172653807),\n",
       " ('S-ZORB.PT_9403.PV', -0.16439638769898515),\n",
       " ('S-ZORB.FC_1005.PV', 0.16190217055525766),\n",
       " ('S-ZORB.TE_2301.PV', -0.15861064673136632),\n",
       " ('S-ZORB.FC_2801.PV', 0.158193849447107),\n",
       " ('S-ZORB.TE_2002.DACA', 0.15486222606151176),\n",
       " ('S-ZORB.SIS_PT_6007.PV', -0.15295056711776606),\n",
       " ('S-ZORB.FT_2431.DACA', -0.15243483285928752),\n",
       " ('S-ZORB.PDI_1102.PV', -0.1513080738679029),\n",
       " ('S-ZORB.FT_3301.PV', 0.14787139058712373),\n",
       " ('S-ZORB.TE_2003.DACA', 0.14477954799283901),\n",
       " ('S-ZORB.PDT_2104.PV', -0.14321735416127276),\n",
       " ('S-ZORB.LI_2104.DACA', -0.14319843875630245),\n",
       " ('S-ZORB.CAL.CANGLIANG.PV', -0.1431348774469485),\n",
       " ('S-ZORB.TE_1501.DACA', 0.13918699203937493),\n",
       " ('S-ZORB.FC_1102.PV', -0.13710397440986158),\n",
       " ('S-ZORB.TE_5101.DACA', -0.1326040482295731),\n",
       " ('S-ZORB.PDT_1004.DACA', 0.13154265940870893),\n",
       " ('S-ZORB.TE_1601.PV', 0.1315103653307669),\n",
       " ('原料性质：溴值', -0.12881903963806185),\n",
       " ('S-ZORB.PT_9401.PV', -0.1252008270009989),\n",
       " ('S-ZORB.PDI_2102.PV', -0.11885010750478466),\n",
       " ('S-ZORB.TE_5003.DACA', 0.11758155886581576),\n",
       " ('S-ZORB.PT_1501.PV', -0.11502120553920707),\n",
       " ('S-ZORB.PT_1102.DACA', 0.11255743160135824),\n",
       " ('S-ZORB.PT_2603.DACA', 0.11159143954113611),\n",
       " ('S-ZORB.PT_1201.PV', 0.10646863747233729),\n",
       " ('S-ZORB.PT_2502.DACA', 0.10487858867013335),\n",
       " ('S-ZORB.PT_2301.PV', 0.10063448165476667),\n",
       " ('S-ZORB.TE_2901.DACA', 0.097967235666283),\n",
       " ('S-ZORB.TE_2401.DACA', -0.093332654019763),\n",
       " ('原料性质：芳烃', 0.09224044923604231),\n",
       " ('S-ZORB.TE_3101.DACA', 0.09005472783421029),\n",
       " ('S-ZORB.FT_1204.DACA.PV', -0.0896594702644829),\n",
       " ('S-ZORB.PT_2501.DACA', 0.08946119178137825),\n",
       " ('S-ZORB.PT_2101.PV', 0.08871497775519506),\n",
       " ('S-ZORB.LI_9102.DACA', 0.08811580612639583),\n",
       " ('S-ZORB.PT_1601.DACA', 0.08550156767243328),\n",
       " ('S-ZORB.PT_5201.DACA', -0.08230351653152411),\n",
       " ('S-ZORB.PC_1202.PV', 0.08155173689355605),\n",
       " ('S-ZORB.FT_2701.DACA', 0.07997012155097384),\n",
       " ('S-ZORB.TE_7506B.DACA', 0.07736179875735413),\n",
       " ('S-ZORB.SIS_TE_2605.PV', 0.07663584639841205),\n",
       " ('S-ZORB.TE_7502B.DACA', 0.07295381962224282),\n",
       " ('S-ZORB.TE_2604.DACA', 0.07250935963560576),\n",
       " ('S-ZORB.PT_2901.DACA', -0.07232219338120492),\n",
       " ('S-ZORB.PT_6009.DACA', 0.07147308381356429),\n",
       " ('S-ZORB.AT-0002.DACA.PV', -0.06779075289033712),\n",
       " ('S-ZORB.TE_6002.DACA', -0.06675858500124107),\n",
       " ('S-ZORB.PDT_3002.DACA', 0.06655454667937506),\n",
       " ('S-ZORB.TE_1203.PV', 0.0640906899857701),\n",
       " ('S-ZORB.PT_2801.PV', 0.06236157010804696),\n",
       " ('S-ZORB.FT_3701.DACA', -0.05832670985009312),\n",
       " ('S-ZORB.TE_5009.DACA', 0.05765548520431504),\n",
       " ('S-ZORB.PDT_2704.DACA', -0.05618377652020236),\n",
       " ('S-ZORB.PC_5101.PV', -0.05617932962203769),\n",
       " ('S-ZORB.TC_2607.PV', 0.05495175531057961),\n",
       " ('S-ZORB.PT_6005.DACA', -0.054588053775453026),\n",
       " ('S-ZORB.PDI_2703A.PV', -0.053773426488590764),\n",
       " ('S-ZORB.TE_2103.PV', 0.05213784098523854),\n",
       " ('S-ZORB.TE_1102.DACA.PV', 0.05209583890532879),\n",
       " ('S-ZORB.TE_6001.DACA', -0.05128313120494427),\n",
       " ('S-ZORB.PT_6002.PV', -0.0512684422239045),\n",
       " ('S-ZORB.PDC_2702.DACA', 0.05087050881779996),\n",
       " ('S-ZORB.TE_5102.PV', -0.048745864574668625),\n",
       " ('S-ZORB.TE_2501.DACA', -0.0457429490221218),\n",
       " ('S-ZORB.PDT_3502.DACA', -0.043745723846020906),\n",
       " ('S-ZORB.TE_1608.PV', -0.04369510162717362),\n",
       " ('S-ZORB.CAL.LINE.PV', 0.043636776957683844),\n",
       " ('待生吸附剂性质：焦炭', 0.04179041701357466),\n",
       " ('S-ZORB.SIS_TE_2606.PV', 0.04128476045117847),\n",
       " ('S-ZORB.PDC_2502.PV', 0.04088703818765207),\n",
       " ('再生吸附剂性质：焦炭', 0.0395323371979747),\n",
       " ('S-ZORB.PT_7505B.DACA', -0.03862897057741696),\n",
       " ('S-ZORB.TE_5006.DACA', 0.0384885292938782),\n",
       " ('S-ZORB.PT_7503B.DACA', -0.038392003188459346),\n",
       " ('S-ZORB.LC_5102.PIDA.PV', 0.03789144208696486),\n",
       " ('S-ZORB.LC_5102.DACA', 0.037678325583359505),\n",
       " ('S-ZORB.AT-0001.DACA.PV', -0.03756875655564205),\n",
       " ('S-ZORB.TE_2104.DACA', 0.03713122252144641),\n",
       " ('S-ZORB.TE_9002.DACA', -0.03663065246968193),\n",
       " ('S-ZORB.FC_2601.PV', 0.03652180361902712),\n",
       " ('S-ZORB.TE_9001.PV', 0.035380390575864146),\n",
       " ('S-ZORB.TE_9301.PV', 0.03258274465906013),\n",
       " ('S-ZORB.FT_5101.PV', -0.03255435371919046),\n",
       " ('S-ZORB.PT_6008.DACA', -0.03195030487538906),\n",
       " ('S-ZORB.LT_2901.DACA', 0.03159160600584424),\n",
       " ('再生吸附剂性质：S', -0.02923176843107358),\n",
       " ('S-ZORB.PT_1604.DACA', 0.02703231901457792),\n",
       " ('S-ZORB.PT_6003.DACA', -0.025907406179258706),\n",
       " ('待生吸附剂性质：S', -0.02461737617860461),\n",
       " ('S-ZORB.PDT_2604.PV', 0.023780176723102554),\n",
       " ('S-ZORB.AC_6001.PV', -0.023263378753731297),\n",
       " ('S-ZORB.TE_2902.DACA', 0.022395170984765962),\n",
       " ('S-ZORB.TE_7508B.DACA', 0.020194085440751793),\n",
       " ('S-ZORB.TE_1605.DACA', -0.01849610999272636),\n",
       " ('S-ZORB.TE_1503.DACA', 0.018167602925143866),\n",
       " ('S-ZORB.FT_9001.PV', 0.016758708002992517),\n",
       " ('S-ZORB.TE_9003.DACA', 0.016278169882270637),\n",
       " ('S-ZORB.PDT_2606.DACA', 0.009954862225241378),\n",
       " ('S-ZORB.FT_2433.DACA', -0.009880371091728334),\n",
       " ('S-ZORB.TE_1502.DACA', -0.00927014260197797),\n",
       " ('S-ZORB.SIS_TE_6009.PV', -0.005980837772217362),\n",
       " ('S-ZORB.TE_7504B.DACA', 0.004269758468651318),\n",
       " ('S-ZORB.TE_6008.DACA', 0.0040964686829010515),\n",
       " ('S-ZORB.LT_3101.DACA', 0.0011432367732332677)]"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_feature_names = [f[0] for f in corr_list[1:31]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['原料性质：辛烷值',\n",
       " '原料性质：硫含量',\n",
       " '原料性质：饱和烃',\n",
       " 'S-ZORB.PT_7107B.DACA',\n",
       " 'S-ZORB.TE_5002.DACA',\n",
       " 'S-ZORB.TE_1201.PV',\n",
       " 'S-ZORB.PT_7103B.DACA',\n",
       " 'S-ZORB.TE_1101.DACA',\n",
       " 'S-ZORB.TE_1105.PV',\n",
       " 'S-ZORB.TE_7108B.DACA',\n",
       " 'S-ZORB.TE_7106B.DACA',\n",
       " '原料性质：烯烃',\n",
       " 'S-ZORB.TC_2801.PV',\n",
       " 'S-ZORB.TE_5008.DACA',\n",
       " 'S-ZORB.TE_5201.DACA',\n",
       " 'S-ZORB.FT_3303.DACA',\n",
       " 'S-ZORB.SIS_TE_2802',\n",
       " 'S-ZORB.FC_5001.DACA',\n",
       " 'S-ZORB.TE_2601.PV',\n",
       " 'S-ZORB.TE_1106.DACA',\n",
       " 'S-ZORB.TE_2603.DACA',\n",
       " 'S-ZORB.PDT_3503.DACA',\n",
       " 'S-ZORB.LT_9101.DACA',\n",
       " 'S-ZORB.AT-0006.DACA.PV',\n",
       " '原料性质：密度',\n",
       " 'S-ZORB.SIS_FT_3202.PV',\n",
       " 'S-ZORB.LC_5101.PV',\n",
       " 'S-ZORB.LT_3801.DACA',\n",
       " 'S-ZORB.TXE_2202A.DACA',\n",
       " 'S-ZORB.PC_3501.DACA']"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_feature_names.append('RON损失')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "#yuanshi_names = list(data_325.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [],
   "source": [
    "#yuanshi_names.pop(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X = data_325[ yuanshi_names ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor as RFR\n",
    "from sklearn.linear_model import LinearRegression as LinearR\n",
    "from sklearn.datasets import load_boston\n",
    "from sklearn.model_selection import KFold, cross_val_score as CVS, train_test_split as TTS\n",
    "from sklearn.metrics import mean_squared_error as MSE\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import mean_absolute_error #平方绝对误差\n",
    "from sklearn.metrics import r2_score#R square\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.tree import DecisionTreeRegressor \n",
    "from sklearn.ensemble import RandomForestRegressor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Xin_value = data_325['产品性质：辛烷值']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Xin_value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtrain,Xtest,Ytrain,Ytest = TTS(data_325[ new_feature_names ], label, test_size=0.3, random_state=111)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "RON_sun_train, RON_sun_test = Xtrain['RON损失'], Xtest['RON损失']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_Xtrain = Xtrain.drop(['RON损失'], axis=1)\n",
    "new_Xtest = Xtest.drop(['RON损失'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 0.12738061224489886 0.3569042059781572 0.24724489795918472 0.8563043000762303\n",
      "2 0.056728316326529755 0.23817706927101473 0.17862244897959034 0.936005841262832\n",
      "3 0.0829357142857136 0.28798561472009954 0.20751700680271956 0.9064417629737005\n",
      "4 0.059158673469387225 0.2432255608882159 0.17265306122448967 0.9332642005645112\n",
      "5 0.06216857142857132 0.24933626176024082 0.17514285714285746 0.9298687906483416\n",
      "6 0.06848775510204054 0.2617016528454502 0.18272108843537385 0.9227402370568402\n",
      "7 0.049491711786754995 0.22246732745901138 0.16970845481049504 0.9441693202733994\n",
      "8 0.0643480707908161 0.25366921529979963 0.17108418367346837 0.9274101379474183\n",
      "9 0.05061914840010055 0.22498699606888517 0.1623356009070281 0.9428974799955171\n",
      "10 0.052191051020407504 0.22845360802667902 0.15549999999999917 0.9411242458804011\n",
      "11 0.05263846348456615 0.22943073788088236 0.16468460111317096 0.9406195281995952\n",
      "12 0.050720975056689176 0.2252131769162035 0.155799319727892 0.9427826112377719\n",
      "13 0.0505940526506452 0.22493121759917006 0.1537441130298257 0.9429257900438011\n",
      "14 0.041287067888379415 0.20319219445731526 0.1509620991253642 0.9534248264828978\n",
      "15 0.05095167800453337 0.22572478376229174 0.15927210884353507 0.9425223595324244\n",
      "16 0.040602686543366504 0.20150108323124843 0.15038265306122228 0.9541968643515597\n",
      "17 0.05077984252524471 0.22534383178876832 0.15896158463385224 0.9427162039411845\n",
      "18 0.051362493701183114 0.2266329492840419 0.1530782312925151 0.9420589259844974\n",
      "19 0.04184791678444149 0.20456763376556295 0.1471106337271745 0.9527921432727074\n",
      "20 0.04843032142857227 0.2200689015480658 0.15686734693877497 0.9453666549990183\n",
      "21 0.048792186126150744 0.22088953376326081 0.15224003887269105 0.9449584421628586\n",
      "22 0.04680170981615693 0.21633702830573626 0.1525927643784765 0.9472038614735803\n",
      "23 0.04063510859920431 0.2015815184961268 0.14730700976042427 0.9541602896332845\n",
      "24 0.038633085317460056 0.19655300892497182 0.13635204081632446 0.9564187348681121\n",
      "25 0.048892344489794945 0.22111613349051432 0.15556326530612255 0.9448454553753607\n",
      "26 0.04449234391981584 0.210932083666321 0.1543249607535302 0.9498090141966401\n",
      "27 0.046439349402313035 0.21549791043607136 0.14862433862433908 0.9476126335180407\n",
      "28 0.04411829706372176 0.21004355991965515 0.14526239067054866 0.9502309694992837\n",
      "29 0.04067727680846179 0.20168608481613645 0.14585503166783598 0.9541127204605745\n",
      "30 0.049326784580498204 0.2220963407634133 0.15623809523809237 0.9443553715878175\n",
      "31 0.04938865764828323 0.22223559041765392 0.15197498354180408 0.944285573730631\n",
      "32 0.04491427574936193 0.21192988404036353 0.154642857142856 0.9493330407459066\n",
      "33 0.05122356590018846 0.22632623776351796 0.15619047619047305 0.9422156478533201\n",
      "34 0.04537373773038694 0.213011121142505 0.1472929171668675 0.9488147302291946\n",
      "35 0.05053128363181931 0.2247916449333011 0.1593702623906687 0.9429965986066958\n",
      "36 0.051617709120684474 0.22719531051649036 0.1548185941043047 0.9417710222157034\n",
      "37 0.05061439826478379 0.224976439354844 0.15400165471593585 0.9429028385348351\n",
      "38 0.04846863799536326 0.22015594017732806 0.15388023630504366 0.9453234307925502\n",
      "39 0.04195056890606356 0.20481838029352628 0.14844322344322308 0.9526763433232082\n",
      "40 0.048375076530612425 0.21994334845730712 0.15252040816326548 0.9454289757410814\n",
      "41 0.047756749505275045 0.21853317712712422 0.15284718765554675 0.9461264989600531\n",
      "42 0.04485228550603926 0.21178358176695203 0.1522133138969859 0.9494029707866382\n",
      "43 0.04894494762751006 0.22123505063056817 0.1522021831988596 0.944786114753904\n",
      "44 0.04580831189914006 0.21402876418635897 0.14658163265305943 0.9483244951906966\n",
      "45 0.04688979743008479 0.21654052145057007 0.15168027210884263 0.9471044914743716\n",
      "46 0.04937052245283686 0.22219478493618355 0.15547692990239534 0.9443060317073748\n",
      "47 0.048506893875703276 0.22024280663781798 0.15045158488927296 0.9452802750453388\n",
      "48 0.04183235145620616 0.20452958577234287 0.15176232993196875 0.9528097022300394\n",
      "49 0.05161169325706272 0.22718207072095878 0.15843815077051257 0.9417778085995852\n",
      "50 0.04435265877551014 0.21060070934237174 0.1536204081632672 0.9499665904103685\n",
      "51 0.0468669193167468 0.21648768860317855 0.14851940776310546 0.9471302998485904\n",
      "52 0.04538100923801427 0.21302818883428143 0.1502001569858682 0.9488065273766599\n",
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      "501 0.04545922391236661 0.2132116880294479 0.14952747566096095 0.9487182948569908\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "503 0.04529849698015369 0.21283443560700813 0.14751775063905298 0.9488996079203676\n",
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      "524 0.045550848718602675 0.21342644802976662 0.1481556706652472 0.9486149346169163\n",
      "525 0.04474033925865874 0.21151912267844422 0.1482258503401813 0.9495292552665735\n",
      "526 0.04672931892119401 0.2161696530995829 0.15003123302555849 0.9472855242960183\n",
      "527 0.044124284464365296 0.21005781219551273 0.14647097548700122 0.9502242152239584\n",
      "528 0.044430794790596966 0.2107861351953609 0.1458601963512994 0.949878446624754\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-204-ffd6f2be0552>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1001\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m     \u001b[0mforest_reg\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mRandomForestRegressor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn_estimators\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m     \u001b[0mforest_reg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_Xtrain\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mYtrain\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m     \u001b[0my_predict\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mforest_reg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_Xtest\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m     \u001b[0mmse_score\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mMSE\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mYtest\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my_predict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\soft\\Anaconda3\\envs\\tf2-gpu\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[0;32m    390\u001b[0m                     \u001b[0mverbose\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mverbose\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mclass_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mclass_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    391\u001b[0m                     n_samples_bootstrap=n_samples_bootstrap)\n\u001b[1;32m--> 392\u001b[1;33m                 for i, t in enumerate(trees))\n\u001b[0m\u001b[0;32m    393\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    394\u001b[0m             \u001b[1;31m# Collect newly grown trees\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\soft\\Anaconda3\\envs\\tf2-gpu\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, iterable)\u001b[0m\n\u001b[0;32m   1030\u001b[0m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_iterating\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_original_iterator\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1031\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1032\u001b[1;33m             \u001b[1;32mwhile\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdispatch_one_batch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1033\u001b[0m                 \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1034\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\soft\\Anaconda3\\envs\\tf2-gpu\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36mdispatch_one_batch\u001b[1;34m(self, iterator)\u001b[0m\n\u001b[0;32m    845\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    846\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 847\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_dispatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtasks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    848\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    849\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\soft\\Anaconda3\\envs\\tf2-gpu\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m_dispatch\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    763\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_lock\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    764\u001b[0m             \u001b[0mjob_idx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 765\u001b[1;33m             \u001b[0mjob\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbatch\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    766\u001b[0m             \u001b[1;31m# A job can complete so quickly than its callback is\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    767\u001b[0m             \u001b[1;31m# called before we get here, causing self._jobs to\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\soft\\Anaconda3\\envs\\tf2-gpu\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36mapply_async\u001b[1;34m(self, func, callback)\u001b[0m\n\u001b[0;32m    206\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mapply_async\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    207\u001b[0m         \u001b[1;34m\"\"\"Schedule a func to be run\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 208\u001b[1;33m         \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mImmediateResult\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    209\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcallback\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    210\u001b[0m             \u001b[0mcallback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\soft\\Anaconda3\\envs\\tf2-gpu\\lib\\site-packages\\joblib\\_parallel_backends.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, batch)\u001b[0m\n\u001b[0;32m    570\u001b[0m         \u001b[1;31m# Don't delay the application, to avoid keeping the input\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    571\u001b[0m         \u001b[1;31m# arguments in memory\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 572\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbatch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    573\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    574\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\soft\\Anaconda3\\envs\\tf2-gpu\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    251\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    252\u001b[0m             return [func(*args, **kwargs)\n\u001b[1;32m--> 253\u001b[1;33m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[0;32m    254\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    255\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__reduce__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\soft\\Anaconda3\\envs\\tf2-gpu\\lib\\site-packages\\joblib\\parallel.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    251\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mparallel_backend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_backend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn_jobs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_n_jobs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    252\u001b[0m             return [func(*args, **kwargs)\n\u001b[1;32m--> 253\u001b[1;33m                     for func, args, kwargs in self.items]\n\u001b[0m\u001b[0;32m    254\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    255\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__reduce__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\soft\\Anaconda3\\envs\\tf2-gpu\\lib\\site-packages\\sklearn\\ensemble\\_forest.py\u001b[0m in \u001b[0;36m_parallel_build_trees\u001b[1;34m(tree, forest, X, y, sample_weight, tree_idx, n_trees, verbose, class_weight, n_samples_bootstrap)\u001b[0m\n\u001b[0;32m    166\u001b[0m                                                         indices=indices)\n\u001b[0;32m    167\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 168\u001b[1;33m         \u001b[0mtree\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcurr_sample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    169\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    170\u001b[0m         \u001b[0mtree\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcheck_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\soft\\Anaconda3\\envs\\tf2-gpu\\lib\\site-packages\\sklearn\\tree\\_classes.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[0;32m   1244\u001b[0m             \u001b[0msample_weight\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1245\u001b[0m             \u001b[0mcheck_input\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcheck_input\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1246\u001b[1;33m             X_idx_sorted=X_idx_sorted)\n\u001b[0m\u001b[0;32m   1247\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1248\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\soft\\Anaconda3\\envs\\tf2-gpu\\lib\\site-packages\\sklearn\\tree\\_classes.py\u001b[0m in \u001b[0;36mfit\u001b[1;34m(self, X, y, sample_weight, check_input, X_idx_sorted)\u001b[0m\n\u001b[0;32m    373\u001b[0m                                            min_impurity_split)\n\u001b[0;32m    374\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 375\u001b[1;33m         \u001b[0mbuilder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtree_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mX_idx_sorted\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    376\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    377\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_outputs_\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mis_classifier\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "#Xtrain,Xtest,Ytrain,Ytest = TTS(X, y, test_size=0.3, random_state=111)\n",
    "for i in range(1,1001):\n",
    "    forest_reg = RandomForestRegressor(n_estimators=i)\n",
    "    forest_reg.fit(new_Xtrain, Ytrain)\n",
    "    y_predict = forest_reg.predict(new_Xtest)\n",
    "    mse_score = MSE(Ytest,y_predict)\n",
    "    rmse_score = np.sqrt(mse_score)\n",
    "    mae_score = mean_absolute_error(Ytest,y_predict)\n",
    "    rr_score = r2_score(Ytest,y_predict)\n",
    "\n",
    "    print(i, mse_score, rmse_score, mae_score, rr_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['原料性质：辛烷值', '原料性质：硫含量', '原料性质：饱和烃', 'S-ZORB.PT_7107B.DACA',\n",
       "       'S-ZORB.TE_5002.DACA', 'S-ZORB.TE_1201.PV', 'S-ZORB.PT_7103B.DACA',\n",
       "       'S-ZORB.TE_1101.DACA', 'S-ZORB.TE_1105.PV', 'S-ZORB.TE_7108B.DACA',\n",
       "       'S-ZORB.TE_7106B.DACA', '原料性质：烯烃', 'S-ZORB.TC_2801.PV',\n",
       "       'S-ZORB.TE_5008.DACA', 'S-ZORB.TE_5201.DACA', 'S-ZORB.FT_3303.DACA',\n",
       "       'S-ZORB.SIS_TE_2802', 'S-ZORB.FC_5001.DACA', 'S-ZORB.TE_2601.PV',\n",
       "       'S-ZORB.TE_1106.DACA', 'S-ZORB.TE_2603.DACA', 'S-ZORB.PDT_3503.DACA',\n",
       "       'S-ZORB.LT_9101.DACA', 'S-ZORB.AT-0006.DACA.PV', '原料性质：密度',\n",
       "       'S-ZORB.SIS_FT_3202.PV', 'S-ZORB.LC_5101.PV', 'S-ZORB.LT_3801.DACA',\n",
       "       'S-ZORB.TXE_2202A.DACA', 'S-ZORB.PC_3501.DACA'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_Xtest.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "RON = new_Xtest['原料性质：辛烷值'] - y_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1.5312\n",
       "301    1.2378\n",
       "52     1.2753\n",
       "15     1.2710\n",
       "240    0.9905\n",
       "        ...  \n",
       "223    1.1555\n",
       "237    1.1247\n",
       "274    1.1935\n",
       "242    1.2178\n",
       "197    1.3065\n",
       "Name: 原料性质：辛烷值, Length: 98, dtype: float64"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.04378549632653038 0.20924984187934378 0.1507571428571403 -0.5718457994243566\n"
     ]
    }
   ],
   "source": [
    "mse_score = MSE(RON_sun_test,RON)\n",
    "rmse_score = np.sqrt(mse_score)\n",
    "mae_score = mean_absolute_error(RON_sun_test,RON)\n",
    "rr_score = r2_score(RON_sun_test,RON)\n",
    "\n",
    "print(mse_score, rmse_score, mae_score, rr_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 8))\n",
    "plt.plot(range(len(Ytest)), Ytest, label='Ytest')\n",
    "plt.plot(range(len(y_predict)), y_predict, label='y_predict')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 8))\n",
    "plt.plot(range(len(RON_sun_test)), RON_sun_test, label='RON_sun_test')\n",
    "plt.plot(range(len(RON)), RON, label='RON')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
